Overview

Dataset statistics

Number of variables22
Number of observations20119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory176.0 B

Variable types

Numeric10
Text5
DateTime3
Categorical4

Alerts

city has constant value ""Constant
state has constant value ""Constant
address_number_start is highly overall correlated with address_number and 3 other fieldsHigh correlation
address_number is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
ward is highly overall correlated with police_districtHigh correlation
police_district is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
latitude is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
longitude is highly overall correlated with address_number_start and 2 other fieldsHigh correlation
street_type is highly imbalanced (52.2%)Imbalance
permit_number has unique valuesUnique
address_number_start has 1240 (6.2%) zerosZeros
address_number has 1240 (6.2%) zerosZeros

Reproduction

Analysis started2023-12-06 14:21:18.841938
Analysis finished2023-12-06 14:21:44.095879
Duration25.25 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

permit_number
Real number (ℝ)

UNIQUE 

Distinct20119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1154034.5
Minimum1000571
Maximum1862206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:44.249329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000571
5-th percentile1024842.4
Q11074664
median1106727
Q31131949.5
95-th percentile1648144.6
Maximum1862206
Range861635
Interquartile range (IQR)57285.5

Descriptive statistics

Standard deviation180318.84
Coefficient of variation (CV)0.15625082
Kurtosis5.2497972
Mean1154034.5
Median Absolute Deviation (MAD)25652
Skewness2.5361042
Sum2.3218021 × 1010
Variance3.2514885 × 1010
MonotonicityNot monotonic
2023-12-06T14:21:44.501723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1556602 1
 
< 0.1%
1086777 1
 
< 0.1%
1086803 1
 
< 0.1%
1086802 1
 
< 0.1%
1086799 1
 
< 0.1%
1086790 1
 
< 0.1%
1086788 1
 
< 0.1%
1086782 1
 
< 0.1%
1086779 1
 
< 0.1%
1086776 1
 
< 0.1%
Other values (20109) 20109
> 99.9%
ValueCountFrequency (%)
1000571 1
< 0.1%
1001307 1
< 0.1%
1002652 1
< 0.1%
1002993 1
< 0.1%
1003612 1
< 0.1%
1004393 1
< 0.1%
1007248 1
< 0.1%
1007265 1
< 0.1%
1007306 1
< 0.1%
1007406 1
< 0.1%
ValueCountFrequency (%)
1862206 1
< 0.1%
1860048 1
< 0.1%
1855208 1
< 0.1%
1854101 1
< 0.1%
1852797 1
< 0.1%
1848630 1
< 0.1%
1848204 1
< 0.1%
1846561 1
< 0.1%
1845400 1
< 0.1%
1844680 1
< 0.1%

account_number
Real number (ℝ)

Distinct3018
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206000.21
Minimum12
Maximum495456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:44.761908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile5350
Q123077
median254059
Q3348425
95-th percentile414151
Maximum495456
Range495444
Interquartile range (IQR)325348

Descriptive statistics

Standard deviation156290.49
Coefficient of variation (CV)0.75869093
Kurtosis-1.6003516
Mean206000.21
Median Absolute Deviation (MAD)144576
Skewness-0.077377047
Sum4.1445182 × 109
Variance2.4426717 × 1010
MonotonicityNot monotonic
2023-12-06T14:21:45.025542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63414 886
 
4.4%
65004 312
 
1.6%
298727 114
 
0.6%
50161 85
 
0.4%
22633 66
 
0.3%
369504 64
 
0.3%
230211 61
 
0.3%
267891 48
 
0.2%
66022 45
 
0.2%
392906 44
 
0.2%
Other values (3008) 18394
91.4%
ValueCountFrequency (%)
12 6
 
< 0.1%
13 21
0.1%
16 4
 
< 0.1%
27 4
 
< 0.1%
28 11
0.1%
46 18
0.1%
51 6
 
< 0.1%
67 20
0.1%
73 17
0.1%
82 2
 
< 0.1%
ValueCountFrequency (%)
495456 1
< 0.1%
494737 1
< 0.1%
494267 1
< 0.1%
493688 1
< 0.1%
493621 1
< 0.1%
493348 1
< 0.1%
493334 1
< 0.1%
493192 1
< 0.1%
493107 1
< 0.1%
493069 1
< 0.1%

site_number
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3757642
Minimum1
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:45.288034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile23
Maximum230
Range229
Interquartile range (IQR)1

Descriptive statistics

Standard deviation18.123494
Coefficient of variation (CV)3.3713335
Kurtosis41.246213
Mean5.3757642
Median Absolute Deviation (MAD)0
Skewness5.9846541
Sum108155
Variance328.46104
MonotonicityNot monotonic
2023-12-06T14:21:45.565950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14715
73.1%
2 2298
 
11.4%
3 608
 
3.0%
4 287
 
1.4%
5 210
 
1.0%
7 121
 
0.6%
6 106
 
0.5%
11 98
 
0.5%
12 94
 
0.5%
19 91
 
0.5%
Other values (90) 1491
 
7.4%
ValueCountFrequency (%)
1 14715
73.1%
2 2298
 
11.4%
3 608
 
3.0%
4 287
 
1.4%
5 210
 
1.0%
6 106
 
0.5%
7 121
 
0.6%
8 65
 
0.3%
9 63
 
0.3%
10 67
 
0.3%
ValueCountFrequency (%)
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
221 1
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
211 2
< 0.1%
Distinct3026
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:46.036255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length67
Median length46
Mean length21.099558
Min length4

Characters and Unicode

Total characters424502
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique681 ?
Unique (%)3.4%

Sample

1st rowTHE LIFEWAY KEFIR SHOP LLC
2nd rowSQUARE KITCHEN, LLC
3rd rowTEMPO CAFE LIMITED
4th rowThe Funky Monkey Juice Bar
5th rowPLEASANT PIZZA, L.L.C.
ValueCountFrequency (%)
inc 8857
 
12.9%
llc 6129
 
8.9%
corporation 1580
 
2.3%
restaurant 1297
 
1.9%
1263
 
1.8%
starbucks 886
 
1.3%
chicago 882
 
1.3%
the 862
 
1.3%
corp 859
 
1.2%
cafe 705
 
1.0%
Other values (3621) 45494
66.1%
2023-12-06T14:21:46.817889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48851
 
11.5%
C 30248
 
7.1%
I 29083
 
6.9%
A 28340
 
6.7%
N 27603
 
6.5%
L 26462
 
6.2%
O 25669
 
6.0%
R 25237
 
5.9%
E 24957
 
5.9%
T 20571
 
4.8%
Other values (68) 137481
32.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 333509
78.6%
Space Separator 48851
 
11.5%
Other Punctuation 26494
 
6.2%
Lowercase Letter 7885
 
1.9%
Decimal Number 6933
 
1.6%
Dash Punctuation 645
 
0.2%
Close Punctuation 85
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 30248
9.1%
I 29083
 
8.7%
A 28340
 
8.5%
N 27603
 
8.3%
L 26462
 
7.9%
O 25669
 
7.7%
R 25237
 
7.6%
E 24957
 
7.5%
T 20571
 
6.2%
S 20236
 
6.1%
Other values (16) 75103
22.5%
Lowercase Letter
ValueCountFrequency (%)
a 909
11.5%
e 905
11.5%
n 779
9.9%
o 653
8.3%
t 602
 
7.6%
r 591
 
7.5%
i 549
 
7.0%
s 513
 
6.5%
c 436
 
5.5%
l 353
 
4.5%
Other values (16) 1595
20.2%
Other Punctuation
ValueCountFrequency (%)
. 11717
44.2%
, 11205
42.3%
' 2276
 
8.6%
& 1097
 
4.1%
# 92
 
0.3%
/ 79
 
0.3%
" 18
 
0.1%
! 6
 
< 0.1%
@ 2
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1325
19.1%
2 983
14.2%
0 893
12.9%
3 797
11.5%
5 796
11.5%
4 709
10.2%
8 466
 
6.7%
6 364
 
5.3%
7 356
 
5.1%
9 244
 
3.5%
Space Separator
ValueCountFrequency (%)
48851
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 645
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 341394
80.4%
Common 83108
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 30248
 
8.9%
I 29083
 
8.5%
A 28340
 
8.3%
N 27603
 
8.1%
L 26462
 
7.8%
O 25669
 
7.5%
R 25237
 
7.4%
E 24957
 
7.3%
T 20571
 
6.0%
S 20236
 
5.9%
Other values (42) 82988
24.3%
Common
ValueCountFrequency (%)
48851
58.8%
. 11717
 
14.1%
, 11205
 
13.5%
' 2276
 
2.7%
1 1325
 
1.6%
& 1097
 
1.3%
2 983
 
1.2%
0 893
 
1.1%
3 797
 
1.0%
5 796
 
1.0%
Other values (16) 3168
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 424502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48851
 
11.5%
C 30248
 
7.1%
I 29083
 
6.9%
A 28340
 
6.7%
N 27603
 
6.5%
L 26462
 
6.2%
O 25669
 
6.0%
R 25237
 
5.9%
E 24957
 
5.9%
T 20571
 
4.8%
Other values (68) 137481
32.4%
Distinct3093
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:47.422565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length88
Median length45
Mean length16.847309
Min length1

Characters and Unicode

Total characters338951
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique670 ?
Unique (%)3.3%

Sample

1st rowLIFEWAY KEFIR SHOP
2nd rowFORK
3rd rowTEMPO CAFE
4th rowThe Funky Monkey Juice Bar
5th rowBOB'S PIZZA
ValueCountFrequency (%)
2393
 
4.3%
cafe 1444
 
2.6%
the 1373
 
2.5%
coffee 1313
 
2.3%
restaurant 1269
 
2.3%
bar 1210
 
2.2%
grill 1046
 
1.9%
starbucks 904
 
1.6%
inc 729
 
1.3%
and 636
 
1.1%
Other values (3514) 43597
78.0%
2023-12-06T14:21:48.353321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35922
 
10.6%
A 27911
 
8.2%
E 25851
 
7.6%
R 20137
 
5.9%
O 19837
 
5.9%
S 19389
 
5.7%
I 17488
 
5.2%
T 17398
 
5.1%
N 16778
 
4.9%
C 14863
 
4.4%
Other values (67) 123377
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 268064
79.1%
Space Separator 35922
 
10.6%
Lowercase Letter 18888
 
5.6%
Other Punctuation 9371
 
2.8%
Decimal Number 6183
 
1.8%
Dash Punctuation 477
 
0.1%
Math Symbol 42
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27911
 
10.4%
E 25851
 
9.6%
R 20137
 
7.5%
O 19837
 
7.4%
S 19389
 
7.2%
I 17488
 
6.5%
T 17398
 
6.5%
N 16778
 
6.3%
C 14863
 
5.5%
L 14452
 
5.4%
Other values (16) 73960
27.6%
Lowercase Letter
ValueCountFrequency (%)
e 2391
12.7%
a 2308
12.2%
o 1514
 
8.0%
n 1499
 
7.9%
r 1377
 
7.3%
i 1364
 
7.2%
t 1155
 
6.1%
l 1110
 
5.9%
s 1080
 
5.7%
u 717
 
3.8%
Other values (15) 4373
23.2%
Other Punctuation
ValueCountFrequency (%)
' 4092
43.7%
& 1842
19.7%
# 1296
 
13.8%
. 890
 
9.5%
, 579
 
6.2%
/ 572
 
6.1%
" 46
 
0.5%
! 43
 
0.5%
@ 8
 
0.1%
; 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1496
24.2%
1 936
15.1%
4 653
10.6%
3 600
9.7%
5 597
 
9.7%
0 569
 
9.2%
7 390
 
6.3%
6 361
 
5.8%
9 293
 
4.7%
8 288
 
4.7%
Space Separator
ValueCountFrequency (%)
35922
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 477
100.0%
Math Symbol
ValueCountFrequency (%)
+ 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 286952
84.7%
Common 51999
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27911
 
9.7%
E 25851
 
9.0%
R 20137
 
7.0%
O 19837
 
6.9%
S 19389
 
6.8%
I 17488
 
6.1%
T 17398
 
6.1%
N 16778
 
5.8%
C 14863
 
5.2%
L 14452
 
5.0%
Other values (41) 92848
32.4%
Common
ValueCountFrequency (%)
35922
69.1%
' 4092
 
7.9%
& 1842
 
3.5%
2 1496
 
2.9%
# 1296
 
2.5%
1 936
 
1.8%
. 890
 
1.7%
4 653
 
1.3%
3 600
 
1.2%
5 597
 
1.1%
Other values (16) 3675
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 338951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35922
 
10.6%
A 27911
 
8.2%
E 25851
 
7.6%
R 20137
 
5.9%
O 19837
 
5.9%
S 19389
 
5.7%
I 17488
 
5.2%
T 17398
 
5.1%
N 16778
 
4.9%
C 14863
 
4.4%
Other values (67) 123377
36.4%
Distinct2999
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-03-14 00:00:00
Maximum2023-12-05 00:00:00
2023-12-06T14:21:48.637164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:48.922790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-11-01 00:00:00
Maximum2024-02-29 00:00:00
2023-12-06T14:21:49.184544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:49.894440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
Distinct2947
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-03-14 00:00:00
Maximum2023-11-16 00:00:00
2023-12-06T14:21:50.391823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:50.869329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2576
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:51.604124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length23
Mean length16.682191
Min length10

Characters and Unicode

Total characters335629
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique447 ?
Unique (%)2.2%

Sample

1st row0 W DIVISION ST
2nd row4600 N LINCOLN AVE
3rd row6 E CHESTNUT ST
4th row2152 W 95TH ST
5th row1659 W 21ST ST
ValueCountFrequency (%)
st 10462
 
12.9%
n 9115
 
11.2%
ave 8057
 
9.9%
w 7266
 
8.9%
e 1879
 
2.3%
s 1859
 
2.3%
0 1240
 
1.5%
clark 1189
 
1.5%
wells 1140
 
1.4%
lincoln 1043
 
1.3%
Other values (1882) 37936
46.7%
2023-12-06T14:21:52.707229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61067
18.2%
E 21334
 
6.4%
S 21165
 
6.3%
A 20969
 
6.2%
N 20513
 
6.1%
T 16688
 
5.0%
1 12449
 
3.7%
L 12244
 
3.6%
I 11534
 
3.4%
W 11416
 
3.4%
Other values (26) 126250
37.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 206366
61.5%
Decimal Number 68196
 
20.3%
Space Separator 61067
 
18.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 21334
10.3%
S 21165
10.3%
A 20969
10.2%
N 20513
9.9%
T 16688
 
8.1%
L 12244
 
5.9%
I 11534
 
5.6%
W 11416
 
5.5%
R 10948
 
5.3%
O 10504
 
5.1%
Other values (15) 49051
23.8%
Decimal Number
ValueCountFrequency (%)
1 12449
18.3%
0 10236
15.0%
2 8736
12.8%
3 8203
12.0%
5 7628
11.2%
4 6593
9.7%
6 4474
 
6.6%
7 4027
 
5.9%
8 3100
 
4.5%
9 2750
 
4.0%
Space Separator
ValueCountFrequency (%)
61067
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206366
61.5%
Common 129263
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 21334
10.3%
S 21165
10.3%
A 20969
10.2%
N 20513
9.9%
T 16688
 
8.1%
L 12244
 
5.9%
I 11534
 
5.6%
W 11416
 
5.5%
R 10948
 
5.3%
O 10504
 
5.1%
Other values (15) 49051
23.8%
Common
ValueCountFrequency (%)
61067
47.2%
1 12449
 
9.6%
0 10236
 
7.9%
2 8736
 
6.8%
3 8203
 
6.3%
5 7628
 
5.9%
4 6593
 
5.1%
6 4474
 
3.5%
7 4027
 
3.1%
8 3100
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61067
18.2%
E 21334
 
6.4%
S 21165
 
6.3%
A 20969
 
6.2%
N 20513
 
6.1%
T 16688
 
5.0%
1 12449
 
3.7%
L 12244
 
3.6%
I 11534
 
3.4%
W 11416
 
3.4%
Other values (26) 126250
37.6%

address_number_start
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1641
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1663.9888
Minimum0
Maximum11208
Zeros1240
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:53.126879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1236
Q32529
95-th percentile4857
Maximum11208
Range11208
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation1628.2859
Coefficient of variation (CV)0.97854382
Kurtosis1.3926959
Mean1663.9888
Median Absolute Deviation (MAD)1034
Skewness1.220358
Sum33477790
Variance2651315.1
MonotonicityNot monotonic
2023-12-06T14:21:53.595405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1240
 
6.2%
200 152
 
0.8%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.5%
20 91
 
0.5%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
2200 75
 
0.4%
Other values (1631) 17999
89.5%
ValueCountFrequency (%)
0 1240
6.2%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

address_number
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1641
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1663.9888
Minimum0
Maximum11208
Zeros1240
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:54.203372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1236
Q32529
95-th percentile4857
Maximum11208
Range11208
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation1628.2859
Coefficient of variation (CV)0.97854382
Kurtosis1.3926959
Mean1663.9888
Median Absolute Deviation (MAD)1034
Skewness1.220358
Sum33477790
Variance2651315.1
MonotonicityNot monotonic
2023-12-06T14:21:54.628105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1240
 
6.2%
200 152
 
0.8%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.5%
20 91
 
0.5%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
2200 75
 
0.4%
Other values (1631) 17999
89.5%
ValueCountFrequency (%)
0 1240
6.2%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

street_direction
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
N
9115 
W
7266 
E
1879 
S
1859 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20119
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowN
3rd rowE
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
N 9115
45.3%
W 7266
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Length

2023-12-06T14:21:54.880677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:21:55.067930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 9115
45.3%
w 7266
36.1%
e 1879
 
9.3%
s 1859
 
9.2%

Most occurring characters

ValueCountFrequency (%)
N 9115
45.3%
W 7266
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20119
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9115
45.3%
W 7266
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 20119
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9115
45.3%
W 7266
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9115
45.3%
W 7266
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

street
Text

Distinct228
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:55.472834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length13
Mean length6.8897062
Min length3

Characters and Unicode

Total characters138614
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.1%

Sample

1st rowDIVISION
2nd rowLINCOLN
3rd rowCHESTNUT
4th row95TH
5th row21ST
ValueCountFrequency (%)
clark 1189
 
5.7%
wells 1140
 
5.5%
lincoln 1043
 
5.0%
division 941
 
4.5%
michigan 729
 
3.5%
randolph 656
 
3.1%
southport 617
 
3.0%
milwaukee 599
 
2.9%
state 536
 
2.6%
halsted 500
 
2.4%
Other values (230) 12879
61.8%
2023-12-06T14:21:56.184917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 12912
 
9.3%
L 11720
 
8.5%
I 11534
 
8.3%
E 11398
 
8.2%
N 11398
 
8.2%
O 10504
 
7.6%
R 10144
 
7.3%
S 8884
 
6.4%
T 6194
 
4.5%
D 6013
 
4.3%
Other values (25) 37913
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 136975
98.8%
Decimal Number 929
 
0.7%
Space Separator 710
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12912
 
9.4%
L 11720
 
8.6%
I 11534
 
8.4%
E 11398
 
8.3%
N 11398
 
8.3%
O 10504
 
7.7%
R 10144
 
7.4%
S 8884
 
6.5%
T 6194
 
4.5%
D 6013
 
4.4%
Other values (15) 36274
26.5%
Decimal Number
ValueCountFrequency (%)
3 312
33.6%
5 226
24.3%
1 143
15.4%
8 60
 
6.5%
6 56
 
6.0%
7 49
 
5.3%
2 44
 
4.7%
9 26
 
2.8%
4 13
 
1.4%
Space Separator
ValueCountFrequency (%)
710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136975
98.8%
Common 1639
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12912
 
9.4%
L 11720
 
8.6%
I 11534
 
8.4%
E 11398
 
8.3%
N 11398
 
8.3%
O 10504
 
7.7%
R 10144
 
7.4%
S 8884
 
6.5%
T 6194
 
4.5%
D 6013
 
4.4%
Other values (15) 36274
26.5%
Common
ValueCountFrequency (%)
710
43.3%
3 312
19.0%
5 226
 
13.8%
1 143
 
8.7%
8 60
 
3.7%
6 56
 
3.4%
7 49
 
3.0%
2 44
 
2.7%
9 26
 
1.6%
4 13
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12912
 
9.3%
L 11720
 
8.5%
I 11534
 
8.3%
E 11398
 
8.2%
N 11398
 
8.2%
O 10504
 
7.6%
R 10144
 
7.3%
S 8884
 
6.4%
T 6194
 
4.5%
D 6013
 
4.3%
Other values (25) 37913
27.4%

street_type
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
ST
10422 
AVE
8057 
RD
 
608
BLVD
 
269
PL
 
255
Other values (4)
 
508

Length

Max length4
Median length2
Mean length2.4490283
Min length2

Characters and Unicode

Total characters49272
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowAVE
3rd rowST
4th rowST
5th rowST

Common Values

ValueCountFrequency (%)
ST 10422
51.8%
AVE 8057
40.0%
RD 608
 
3.0%
BLVD 269
 
1.3%
PL 255
 
1.3%
PKWY 199
 
1.0%
DR 196
 
1.0%
CT 72
 
0.4%
HWY 41
 
0.2%

Length

2023-12-06T14:21:56.606813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:21:56.951965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
st 10422
51.8%
ave 8057
40.0%
rd 608
 
3.0%
blvd 269
 
1.3%
pl 255
 
1.3%
pkwy 199
 
1.0%
dr 196
 
1.0%
ct 72
 
0.4%
hwy 41
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 10494
21.3%
S 10422
21.2%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49272
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10494
21.3%
S 10422
21.2%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 49272
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10494
21.3%
S 10422
21.2%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10494
21.3%
S 10422
21.2%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

city
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
CHICAGO
20119 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140833
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHICAGO
2nd rowCHICAGO
3rd rowCHICAGO
4th rowCHICAGO
5th rowCHICAGO

Common Values

ValueCountFrequency (%)
CHICAGO 20119
100.0%

Length

2023-12-06T14:21:57.276092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:21:57.497653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
chicago 20119
100.0%

Most occurring characters

ValueCountFrequency (%)
C 40238
28.6%
H 20119
14.3%
I 20119
14.3%
A 20119
14.3%
G 20119
14.3%
O 20119
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 140833
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 40238
28.6%
H 20119
14.3%
I 20119
14.3%
A 20119
14.3%
G 20119
14.3%
O 20119
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 140833
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 40238
28.6%
H 20119
14.3%
I 20119
14.3%
A 20119
14.3%
G 20119
14.3%
O 20119
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 40238
28.6%
H 20119
14.3%
I 20119
14.3%
A 20119
14.3%
G 20119
14.3%
O 20119
14.3%

state
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
IL
20119 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters40238
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL 20119
100.0%

Length

2023-12-06T14:21:57.719889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T14:21:57.980929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
il 20119
100.0%

Most occurring characters

ValueCountFrequency (%)
I 20119
50.0%
L 20119
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40238
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 20119
50.0%
L 20119
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40238
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 20119
50.0%
L 20119
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 20119
50.0%
L 20119
50.0%

zip_code
Real number (ℝ)

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60625.51
Minimum60601
Maximum60707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:58.208742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60601
5-th percentile60602
Q160610
median60618
Q360647
95-th percentile60657
Maximum60707
Range106
Interquartile range (IQR)37

Descriptive statistics

Standard deviation19.888105
Coefficient of variation (CV)0.00032804846
Kurtosis-0.68202376
Mean60625.51
Median Absolute Deviation (MAD)11
Skewness0.70050936
Sum1.2197246 × 109
Variance395.53672
MonotonicityNot monotonic
2023-12-06T14:21:58.517685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60611 1840
 
9.1%
60654 1768
 
8.8%
60622 1724
 
8.6%
60614 1550
 
7.7%
60657 1420
 
7.1%
60607 1157
 
5.8%
60613 981
 
4.9%
60610 949
 
4.7%
60647 883
 
4.4%
60618 835
 
4.2%
Other values (43) 7012
34.9%
ValueCountFrequency (%)
60601 652
3.2%
60602 410
 
2.0%
60603 330
 
1.6%
60604 234
 
1.2%
60605 729
3.6%
60606 385
 
1.9%
60607 1157
5.8%
60608 207
 
1.0%
60609 31
 
0.2%
60610 949
4.7%
ValueCountFrequency (%)
60707 47
 
0.2%
60661 504
 
2.5%
60660 199
 
1.0%
60659 189
 
0.9%
60657 1420
7.1%
60656 15
 
0.1%
60655 3
 
< 0.1%
60654 1768
8.8%
60653 20
 
0.1%
60651 3
 
< 0.1%

ward
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.48069
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:58.876632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q126
median42
Q343
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.405511
Coefficient of variation (CV)0.52112933
Kurtosis-0.67684772
Mean31.48069
Median Absolute Deviation (MAD)5
Skewness-0.95164101
Sum633360
Variance269.14078
MonotonicityNot monotonic
2023-12-06T14:21:59.161038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
42 5554
27.6%
1 1580
 
7.9%
27 1538
 
7.6%
47 1476
 
7.3%
44 1427
 
7.1%
2 1374
 
6.8%
43 1186
 
5.9%
32 1114
 
5.5%
4 666
 
3.3%
25 471
 
2.3%
Other values (34) 3733
18.6%
ValueCountFrequency (%)
1 1580
7.9%
2 1374
6.8%
3 289
 
1.4%
4 666
3.3%
5 103
 
0.5%
6 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 13
 
0.1%
11 332
 
1.7%
ValueCountFrequency (%)
50 104
 
0.5%
49 148
 
0.7%
48 361
 
1.8%
47 1476
 
7.3%
46 335
 
1.7%
45 238
 
1.2%
44 1427
 
7.1%
43 1186
 
5.9%
42 5554
27.6%
41 81
 
0.4%

police_district
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.840897
Minimum0
Maximum25
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:59.388740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median18
Q319
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.866289
Coefficient of variation (CV)0.49608701
Kurtosis-0.40080436
Mean13.840897
Median Absolute Deviation (MAD)2
Skewness-1.0015661
Sum278465
Variance47.145925
MonotonicityNot monotonic
2023-12-06T14:21:59.601334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 5339
26.5%
19 4255
21.1%
1 3656
18.2%
12 2388
11.9%
14 1788
 
8.9%
20 760
 
3.8%
16 418
 
2.1%
17 373
 
1.9%
24 309
 
1.5%
2 238
 
1.2%
Other values (13) 595
 
3.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3656
18.2%
2 238
 
1.2%
3 10
 
< 0.1%
4 15
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 44
 
0.2%
9 223
 
1.1%
ValueCountFrequency (%)
25 149
 
0.7%
24 309
 
1.5%
22 27
 
0.1%
20 760
 
3.8%
19 4255
21.1%
18 5339
26.5%
17 373
 
1.9%
16 418
 
2.1%
15 7
 
< 0.1%
14 1788
 
8.9%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2568
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.910369
Minimum41.69067
Maximum42.019421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-12-06T14:21:59.855654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41.69067
5-th percentile41.865345
Q141.885257
median41.902442
Q341.93924
95-th percentile41.978062
Maximum42.019421
Range0.32875146
Interquartile range (IQR)0.053982374

Descriptive statistics

Standard deviation0.037934124
Coefficient of variation (CV)0.00090512503
Kurtosis1.7963231
Mean41.910369
Median Absolute Deviation (MAD)0.020466559
Skewness-0.10655293
Sum843194.72
Variance0.0014389978
MonotonicityNot monotonic
2023-12-06T14:22:00.148734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88200199 98
 
0.5%
41.88197573 96
 
0.5%
41.90405052 86
 
0.4%
41.87801449 81
 
0.4%
41.88460018 69
 
0.3%
41.8825402 53
 
0.3%
41.89678605 40
 
0.2%
41.90186736 40
 
0.2%
41.87949547 37
 
0.2%
41.88216417 37
 
0.2%
Other values (2558) 19482
96.8%
ValueCountFrequency (%)
41.69066951 1
 
< 0.1%
41.69139989 2
 
< 0.1%
41.69245222 1
 
< 0.1%
41.69920305 10
< 0.1%
41.70289718 1
 
< 0.1%
41.70356373 2
 
< 0.1%
41.71874411 1
 
< 0.1%
41.72107515 10
< 0.1%
41.72112515 1
 
< 0.1%
41.72177014 1
 
< 0.1%
ValueCountFrequency (%)
42.01942097 12
0.1%
42.0193885 5
 
< 0.1%
42.01934594 4
 
< 0.1%
42.01933013 3
 
< 0.1%
42.01932963 2
 
< 0.1%
42.0193098 4
 
< 0.1%
42.01927235 1
 
< 0.1%
42.0174068 8
< 0.1%
42.01615704 15
0.1%
42.01615267 15
0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2568
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.655297
Minimum-87.834308
Maximum-87.535139
Zeros0
Zeros (%)0.0%
Negative20119
Negative (%)100.0%
Memory size157.3 KiB
2023-12-06T14:22:00.459431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-87.834308
5-th percentile-87.709805
Q1-87.672586
median-87.648962
Q3-87.629547
95-th percentile-87.624225
Maximum-87.535139
Range0.29916895
Interquartile range (IQR)0.043038819

Descriptive statistics

Standard deviation0.033387503
Coefficient of variation (CV)-0.00038089544
Kurtosis5.1251329
Mean-87.655297
Median Absolute Deviation (MAD)0.020372619
Skewness-1.7593019
Sum-1763536.9
Variance0.0011147254
MonotonicityNot monotonic
2023-12-06T14:22:00.827329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.63103164 98
 
0.5%
-87.63397185 96
 
0.5%
-87.62874675 86
 
0.4%
-87.63318903 81
 
0.4%
-87.62798897 69
 
0.3%
-87.62453095 53
 
0.3%
-87.62828088 40
 
0.2%
-87.62849214 40
 
0.2%
-87.63382966 37
 
0.2%
-87.62451427 37
 
0.2%
Other values (2558) 19482
96.8%
ValueCountFrequency (%)
-87.8343079 1
 
< 0.1%
-87.82625503 9
< 0.1%
-87.82618448 1
 
< 0.1%
-87.82167425 5
 
< 0.1%
-87.82042719 4
 
< 0.1%
-87.81865423 16
0.1%
-87.81795264 4
 
< 0.1%
-87.81783297 3
 
< 0.1%
-87.81729036 11
0.1%
-87.8172596 1
 
< 0.1%
ValueCountFrequency (%)
-87.53513895 2
 
< 0.1%
-87.55117213 1
 
< 0.1%
-87.55124869 1
 
< 0.1%
-87.55161886 9
< 0.1%
-87.56729719 2
 
< 0.1%
-87.58184369 4
< 0.1%
-87.58390766 1
 
< 0.1%
-87.58502961 2
 
< 0.1%
-87.58781452 8
< 0.1%
-87.58797399 7
< 0.1%
Distinct2568
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-12-06T14:22:01.326294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length40
Median length39
Mean length39.105572
Min length35

Characters and Unicode

Total characters786765
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique446 ?
Unique (%)2.2%

Sample

1st row(41.90405051948726, -87.62874675447662)
2nd row(41.964902360748326, -87.68627917084095)
3rd row(41.89843137207629, -87.6280091630558)
4th row(41.72112514525722, -87.67692855592173)
5th row(41.853999857174315, -87.66845450091006)
ValueCountFrequency (%)
41.88200198545344 98
 
0.2%
87.6310316367502 98
 
0.2%
41.881975727713886 96
 
0.2%
87.63397184627037 96
 
0.2%
41.90405051948726 86
 
0.2%
87.62874675447662 86
 
0.2%
41.878014487249544 81
 
0.2%
87.63318903001444 81
 
0.2%
87.62798896732363 69
 
0.2%
41.884600177780484 69
 
0.2%
Other values (5126) 39378
97.9%
2023-12-06T14:22:02.035135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 84475
10.7%
4 75182
9.6%
7 74178
9.4%
6 72034
9.2%
1 70021
8.9%
9 62719
8.0%
2 53712
 
6.8%
3 53210
 
6.8%
5 52653
 
6.7%
0 47748
 
6.1%
Other values (6) 140833
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 645932
82.1%
Other Punctuation 60357
 
7.7%
Open Punctuation 20119
 
2.6%
Space Separator 20119
 
2.6%
Dash Punctuation 20119
 
2.6%
Close Punctuation 20119
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 84475
13.1%
4 75182
11.6%
7 74178
11.5%
6 72034
11.2%
1 70021
10.8%
9 62719
9.7%
2 53712
8.3%
3 53210
8.2%
5 52653
8.2%
0 47748
7.4%
Other Punctuation
ValueCountFrequency (%)
. 40238
66.7%
, 20119
33.3%
Open Punctuation
ValueCountFrequency (%)
( 20119
100.0%
Space Separator
ValueCountFrequency (%)
20119
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20119
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 786765
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 84475
10.7%
4 75182
9.6%
7 74178
9.4%
6 72034
9.2%
1 70021
8.9%
9 62719
8.0%
2 53712
 
6.8%
3 53210
 
6.8%
5 52653
 
6.7%
0 47748
 
6.1%
Other values (6) 140833
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 786765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 84475
10.7%
4 75182
9.6%
7 74178
9.4%
6 72034
9.2%
1 70021
8.9%
9 62719
8.0%
2 53712
 
6.8%
3 53210
 
6.8%
5 52653
 
6.7%
0 47748
 
6.1%
Other values (6) 140833
17.9%

Interactions

2023-12-06T14:21:40.757066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:22.150582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:24.328234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:26.308178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:28.272024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:30.408132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:32.474117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:34.466539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:36.429742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:38.506176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:40.963827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:22.360934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:24.512335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:26.484975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:28.448078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:30.623997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:32.653464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:34.641564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:36.671590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:38.740776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:41.202473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:22.578204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:24.712590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:26.646195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:28.615889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:30.770222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:32.868636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:34.812953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:36.873663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:38.962007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:41.399076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:22.845653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:24.913326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:26.838574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:29.018683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:31.001556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:33.068728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:34.970865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:37.071223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:39.150213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:41.571109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:23.084087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:25.079578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:27.051383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:29.246847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:31.324799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:33.231024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:35.130885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:37.225336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:39.353781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:41.835952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:23.297528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:25.242197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:27.263781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:29.450828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:31.517630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:33.426018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:35.366548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:37.393638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:39.654779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:42.009426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:23.537441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:25.411617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:27.421541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:29.619834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:31.726440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:33.654860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:35.561648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:37.609262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:39.862617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:42.247040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:23.822902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:25.593882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:27.638935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:29.793941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:31.911025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:33.892208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:35.745029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:37.866138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:40.081252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:42.462696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:23.974622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:25.838953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:27.874022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:29.981369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:32.085800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:34.075860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:36.028085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:38.064152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:40.311699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:42.652741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:24.147086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:26.086953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:28.094450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:30.220344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:32.281880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:34.292519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:36.233556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:38.275141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-06T14:21:40.537383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-06T14:22:02.228806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
permit_numberaccount_numbersite_numberaddress_number_startaddress_numberzip_codewardpolice_districtlatitudelongitudestreet_directionstreet_type
permit_number1.0000.4730.017-0.174-0.174-0.018-0.082-0.156-0.1430.0390.0370.041
account_number0.4731.000-0.174-0.123-0.123-0.046-0.108-0.158-0.1360.0210.0750.067
site_number0.017-0.1741.000-0.122-0.122-0.0970.075-0.051-0.0740.1440.0690.054
address_number_start-0.174-0.123-0.1221.0001.0000.3780.1760.5130.707-0.7450.2940.210
address_number-0.174-0.123-0.1221.0001.0000.3780.1760.5130.707-0.7450.2940.210
zip_code-0.018-0.046-0.0970.3780.3781.0000.1770.4520.450-0.4760.3070.222
ward-0.082-0.1080.0750.1760.1760.1771.0000.5100.453-0.0490.2860.167
police_district-0.156-0.158-0.0510.5130.5130.4520.5101.0000.813-0.3390.3830.186
latitude-0.143-0.136-0.0740.7070.7070.4500.4530.8131.000-0.6330.3850.212
longitude0.0390.0210.144-0.745-0.745-0.476-0.049-0.339-0.6331.0000.3210.268
street_direction0.0370.0750.0690.2940.2940.3070.2860.3830.3850.3211.0000.241
street_type0.0410.0670.0540.2100.2100.2220.1670.1860.2120.2680.2411.000

Missing values

2023-12-06T14:21:42.973887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-06T14:21:43.648284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

permit_numberaccount_numbersite_numberlegal_namedoing_business_as_nameissued_dateexpiration_datepayment_dateaddressaddress_number_startaddress_numberstreet_directionstreetstreet_typecitystatezip_codewardpolice_districtlatitudelongitudelocation
015566023289921THE LIFEWAY KEFIR SHOP LLCLIFEWAY KEFIR SHOP07/16/202102/28/202207/16/20210 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.0118.041.904051-87.628747(41.90405051948726, -87.62874675447662)
115345562527421SQUARE KITCHEN, LLCFORK07/16/202102/28/202207/16/20214600 N LINCOLN AVE46004600NLINCOLNAVECHICAGOIL60625.04719.041.964902-87.686279(41.964902360748326, -87.68627917084095)
21540360239571TEMPO CAFE LIMITEDTEMPO CAFE07/16/202102/28/202207/15/20216 E CHESTNUT ST66ECHESTNUTSTCHICAGOIL60611.0218.041.898431-87.628009(41.89843137207629, -87.6280091630558)
315499374751751The Funky Monkey Juice BarThe Funky Monkey Juice Bar07/22/202102/28/202207/22/20212152 W 95TH ST21522152W95THSTCHICAGOIL60643.01922.041.721125-87.676929(41.72112514525722, -87.67692855592173)
415313184314221PLEASANT PIZZA, L.L.C.BOB'S PIZZA07/23/202102/28/202207/23/20211659 W 21ST ST16591659W21STSTCHICAGOIL60608.02512.041.854000-87.668455(41.853999857174315, -87.66845450091006)
515388983173831AJD RESTAURANT GROUP, LLCSULLY'S HOUSE07/23/202102/28/202207/22/20211501 N DAYTON ST15011501NDAYTONSTCHICAGOIL60642.0218.041.908620-87.649295(41.90861972732258, -87.64929457094323)
615488492113241ITALIAN RISTORANTE-HUBBARD, LLCVERMILION12/18/202102/28/202212/18/202110 W HUBBARD ST1010WHUBBARDSTCHICAGOIL60654.04218.041.890169-87.628394(41.89016858549094, -87.62839433601951)
71540948180391SAVBET, INC.BOURBON CAFE'12/14/202102/28/202206/07/20214768 N LINCOLN AVE47684768NLINCOLNAVECHICAGOIL60625.04719.041.968309-87.688536(41.968308771943036, -87.68853600406185)
815581313033921MCM PUB & LIQUOR, INC.MCM PUB AND LIQUORS01/11/202202/28/202201/11/20220 N CICERO AVE00NCICEROAVECHICAGOIL60641.04515.041.880561-87.745427(41.880560880394704, -87.74542684331763)
916439694631881ETTA RIVER NORTH, LLCETTA03/09/202202/28/202302/24/20220 N CLARK ST00NCLARKSTCHICAGOIL60654.021.041.882002-87.631032(41.88200198545344, -87.6310316367502)
permit_numberaccount_numbersite_numberlegal_namedoing_business_as_nameissued_dateexpiration_datepayment_dateaddressaddress_number_startaddress_numberstreet_directionstreetstreet_typecitystatezip_codewardpolice_districtlatitudelongitudelocation
2010918374294050882ROTI RESTAURANTS, LLCROTI MODERN MEDITERRANEAN07/18/202302/29/202407/18/202380 E LAKE ST8080ELAKESTCHICAGOIL60601.0421.041.885847-87.625095(41.885847208704554, -87.62509485736301)
201101680974271271LOUIE'S PUB, INC.LOUIE'S PUB07/18/202302/28/202307/18/20230 W NORTH AVE00WNORTHAVECHICAGOIL60622.0218.041.911332-87.628955(41.911331719638085, -87.6289547607781)
2011118082763811231720 SUSHI MIKE's LLCTANOSHII SUSHI MIKE'S07/19/202302/29/202405/18/2023720 W RANDOLPH ST720720WRANDOLPHSTCHICAGOIL60661.02712.041.884523-87.646521(41.88452285187888, -87.64652058438048)
20112179751332209911721 W. DIVISION CORP.Mama Delia & Bordel07/19/202302/29/202407/19/20231721 W DIVISION ST17211721WDIVISIONSTCHICAGOIL60622.0112.041.903181-87.670876(41.903180986329666, -87.67087633768513)
2011318380274152101AREPA GEORGE LPAREPA GEORGE07/24/202302/29/202407/24/20230 N KEDZIE AVE00NKEDZIEAVECHICAGOIL60651.02611.041.881016-87.706271(41.881015948678254, -87.7062708302215)
2011417796093933071ALULU LLCALULU07/25/202302/29/202407/25/20230 S LAFLIN ST00SLAFLINSTCHICAGOIL60608.02512.041.881448-87.664564(41.8814479951026, -87.6645642415278)
2011518248484919751DIVISION BAR AND RESTAURANT LLCDesert Hawk07/26/202302/29/202407/26/20232049 W DIVISION ST20492049WDIVISIONSTCHICAGOIL60622.0112.041.903046-87.679195(41.903045577773014, -87.67919511353082)
2011618075684942671NESH MEDITERRANEAN GRILL LLCNESH MEDITERRANEAN GRILL07/26/202302/29/202407/26/2023205 W MONROE ST205205WMONROESTCHICAGOIL60606.0421.041.880548-87.633981(41.880547897806615, -87.63398066695044)
201171805009210101KITSCH'N ON ROSCOE, INC.KITSCH'N ON ROSCOE07/28/202302/29/202407/28/20232005 W ROSCOE ST20052005WROSCOESTCHICAGOIL60618.03219.041.943105-87.678679(41.94310456354472, -87.67867885196078)
20118178309442454321253 W 18TH STREET, LLCSushi Hoshi07/28/202302/29/202407/28/20230 S LAFLIN ST00SLAFLINSTCHICAGOIL60608.02512.041.881448-87.664564(41.8814479951026, -87.6645642415278)